As a pioneer work, a three-convolutional-layer network is used in SRCNN [1]} to learn the LR-HR mapping for single image SR. Since then, motivated by the promising performance of CNN, several CNN-based SR methods have been proposed.
Realizing the importance of the network depth, [2]} designed a 20-layer network with residual blocks. [3]} then combined residual learning and dense connection to extend the network depth to 100 layers. To enlarge the receptive field, [4]}, [5]} proposed to utilize non-local self-similarity in SR. Recently, channel attention and second-order channel attention were further introduced by RCAN [6]} and SAN [7]} to exploit feature correlation for improved performance. Some GAN-based methods [8]} were proposed to produce higher perceptual quality. Among existing methods, setting downsampling operation as a known prior is the most popular choice. However, they have poor generalization ability due to the domain gap between real-world LR images and clean-LR images used for training (typically clean bicubically downsampled images).
